Advantages and Challenges in AI Network Monitoring

AI is now a transformative force because it is able to empower businesses and software professionals to improve and push boundaries that were once considered impossible. As AI becomes more accepted, the complexity of its management is increasing. Network monitoring is needed to monitor AI within the company.

Network monitoring ensures optimal network performance and resource efficiency and provides a smooth user experience. Below we will discuss the advantages and challenges of AI network monitoring in full!

What is AI monitoring?

AI monitoring is an important process in artificial intelligence that involves continuous observation and analysis. This serves as a proactive step to maintain the health and efficiency of AI applications.

Information technology (IT) companies and teams deploy and operate AI-based solutions for everyday use. For example, natural language processing, computer vision or machine learning algorithms, and deep learning.

Advantages of AI Network Monitoring

Here are some of the advantages of implementing AI-based network monitoring applications:

1. Improved and simplified IT monitoring

Networks are constantly evolving in terms of the density of connected components and equipment and their virtual nature. Monitoring in this increasingly congested environment is certainly a complicated and often inefficient process. Identifying issues before a company’s operations are negatively impacted can be a major challenge, which is why IT teams are looking for different monitoring tools to isolate the sources of downtime in the network.

Network monitoring AI promises to eliminate the difficulties associated with tracking network activity by providing a clear view of all elements in the infrastructure while it is operating, as it can simplify IT operations. In addition, AI monitoring can also speed up and improve root cause analysis to get to the source of the problem quickly.

2. Gain insight into security

AI monitoring can also shed light on potential security issues by identifying threats quickly and accurately and driving faster remediation. The system also reduces the false alerts that overwhelm security analysis, thus assisting companies in improving the overall security of their networks.

3. Support Automation

AI can inform systems that can automate processes that were previously manual. While some of these processes may be supporting elements of repetitive continuous maintenance, some companies are exploring the use of automation for higher-level tasks. For example, AI can be used to trigger automated mitigation and remediation responses.

4. Monitoring AI Model performance

AI-based network monitoring applications will ensure AI models provide accurate and reliable results. Metrics such as accuracy and precision are often used to evaluate model performance. By continuously tracking these metrics, IT teams can detect changes in model behavior, identify potential deviations, or degradation in performance. In addition, network monitoring is also able to take corrective action to maintain the accuracy of the model.

5. Monitor Resources

Every network certainly requires significant resources to keep its performance running smoothly. Network resources include CPU, GPU, memory, and storage. Monitoring resource consumption ensures that the AI system has sufficient resources to handle the workload efficiently without experiencing bottlenecks or interruptions.

6. Monitor the use of API

AI models are typically accessed through APIs (application programming interfaces). Because of this, AI monitoring also includes monitoring API usage which includes involving tracking metrics such as request rate and response time. This monitoring helps IT engineers to detect unusual patterns, such as sudden spikes in API calls, that may indicate increased demand or potential issues.

Challenges of AI Network Monitoring

Although network monitoring AI is useful in production environments, some challenges remain that can hinder its successful implementation. Basically, IT operations teams need to overcome the learning curve to use AI monitoring effectively. 

In addition, at the beginning of the implementation of network monitoring AI, the network operation team must also consider the challenges as below:

1. Data Quality

When distinguishing between a harmless anomaly and an actual network problem, the AI will only display the results of the data it obtains. If some real-time data is not captured or there is an issue with data integrity, the network monitoring AI may not display accurate results.

Many companies use synthetic data, which is created to simulate activities on the production network, as an alternative to real-time data. Although synthetic data is similar to actual network activity, it is not identical and may miss the actual problem or flag an issue that is not the actual problem.

2. Integrations

As with any technology that is newly implemented into a production environment with existing monitoring and management tools, its integration can be complicated. If an enterprise’s IT team is unable to synchronize AI network monitoring tools with other management services and security systems, then the results may be substandard.

3. Ethical Issues

As AI has evolved and become smarter, some ethical issues may arise in terms of abuse and operational integrity. The same thing also happens with network monitoring where AI technology analyzes large amounts of data. AI continues to develop, so monitoring must also continue to develop.

4. Problem Detection Requires The Team To Remain Proactive

Current network monitoring approaches to problem detection in AI data are largely reactive. IT teams often have to address issues after they have occurred. Therefore, downtime has already occurred and the efficiency of network work is reduced. If the problem is easy to handle, then downtime can be quickly overcome. On the other hand, downtime can also be long if the problem is difficult.

Complex data structures make it difficult to predict and prevent problems before they impact the system. A proactive approach is needed from the IT team to be able to find problems before they happen and disrupt. Another solution is to use a very sophisticated network monitoring tool, and the price is certainly higher.

Use Quality Network Monitoring

In order to continue to compete and become a competent company in the midst of high market competition, every company needs to keep up with technological developments. Therefore, AI network monitoring is needed in accordance with the times. Make sure to use quality network monitoring such as Netmonk from Telkom Indonesia.

The product, Netmonk Prime, is able to meet the needs of network monitoring applications, web/API monitoring, and server monitoring with only 1 application, making it more practical. Its services have been trusted by more than 15 large companies, such as t-money, Pelita Air Service, PeduliLindungi, and others. Come on, visit the Netmonk website to try the service!